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License: MIT License
Causal Neural Nerwork
License: MIT License
Can you provide yaml configuration files related to the "netsim" experiment in CUTS?
May I ask which 10 individuals in the Netsim experiment have specific data in the dataset? The results obtained by each individual seem to differ significantly.
Dear Authors,
I would like to ask is there a available settings or possibility that CUTS_Plus could be trained to without causal groundtruth graphs. So in this cases we can save the graphs generated by the model, and use svm to classify like for gender classification?
Thanks a lot,
May I sask if it is possible that the output is not a DAG?
If that is the case, how can we handle it or can we consider it as a causal graph?
Thanks!
Hello, the code you provided to calculate TN seems to be incorrect. You used ' np.mean((1-causal_graph) * (1-causal_graph)) ' instead of ' np.mean((1-causal_graph) * (1-true_cm)) ', which should be used to calculate TN. (Refer to line-43 in misc.py) .
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